Call for Papers – Machine Learning for Big Data in Industrial Processes

Special Issue on Machine Learning for Big Data Analytics in Manufacturing and Logistics Processes

Applied Mathematical Modelling invites submissions of original contributions to machine learning research for Big Data Analytics for Optimization of Manufacturing and Logistics Processes.

1. Summary and Scope

Machine learning is continuously enhancing its power in a wide range of applications and has been pushed to the forefront in recent years partly owing to the advent of big data. Thus, machine learning techniques have generated a huge societal impact in a wide range of applications such as computer vision, speech processing, natural language understanding, neuroscience, health, and Internet of Things and business process improvement. Moreover, in the context of big data, machine learning algorithms enable to uncover more fine-grained or complex patterns and make more timely and accurate predictions than ever before, e.g. for sales, marketing and tailor-made advertising applications for customers.

The data comes from different sources and in different forms and formats (i.e. structured or unstructured) such as consisting of a complex mixture of cross-media data content. For example, text, images, videos, audio, graphics, process signals, and time series sequences in logistics and manufacturing processes. The complexity, size, variety, and uncertainty (noise) in the data make it challenging to analyze the data and build models with it using traditional approaches. Machine learning methods have extensively been used in many industrial application areas such as pattern recognition, object and product identification and steering, predictive maintenance, scheduling and material flow control, predictive analytics in supply chains for logistics planning purposes using industry 4.0 environment, and statistical process control. Machine Learning is programming computers to optimize a performance criterion using example data or past experience. They are most useful when learning is needed in the absence of human expertise, or humans are unable to explain their expertise, or solution changes over time, or solution needs to be adopted in particular cases[1]

This special issue will focus on brand-new research results and shared recent advances in this research area. We solicit original contributions that have a strong emphasis on data analytics using machine learning techniques.

The list of possible topics includes, but is not limited to:

Machine learning methods for

  • Business process improvement and optimization
  • Analysis of real-time business process data
  • Real-time data analysis in a statistical process and quality control
  • Predictive analytics in supply chains
  • Machine learning methods in process optimization and quality control
  • Predictive maintenance
  • Logistics and manufacturing process optimization
  • Data analytics in manufacturing and logistics processes
  • Industrial analysis and mining applications via machine learning methods


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